Upcoming Event: Oden Institute Seminar

How Generative Pretraining Transformer (GPT) have Transformed Scientific Computation

Amir Barati Farimini, Russel V. Trader Associate Professor of Mechanical Engineering, Carnegie-Mellon University

Abstract

With the rise of Artificial Intelligence (AI) and Machine Learning (ML) in recent years, many complex problems in vision and computer science have been solved that were intractable for decades. In mechanical engineering (ME), complex problems still exist that conventional techniques could not offer viable solutions. Recent advances in AI has provided us with opportunities to merge and apply them to challenges in mechanical engineering, however; to accurately model and predict an engineering system, the representation and formulation of that problem into AI frameworks remain a challenge. The domain knowledge of ME is needed to represent and beneficially use AI algorithms to achieve robust solutions. To this end, I will talk about how effectively different areas of engineering can take advantage of AI to find solutions by integrating the physics and engineering domain knowledge. I will focus on examples in transport phenomena, additive manufacturing and material discovery. I will then show how generative, pretraining and transformers (GPT) can be used in scientific computing.

Biography

Professor Barati Farimini is Russel V. Trader Associate Professor of Mechanical Engineering at Carnegie M joined the Department of Mechanical Engineering at Carnegie Mellon University in the fall of 2018. He was previously a postdoctoral fellow at Stanford University. He received his PhD in Mechanical Engineering in 2015 from University of Illinois at Urbana-Champaign.
His lab at CMU focuses on the problems at the interface of Mechanical Engineering, data science and machine learning. His lab uses the state-of-the-art deep learning and machine learning algorithms and tools to learn, infer and predict the physical phenomena pertinent to mechanical engineering. Currently, he is teaching AI and ML to a large class of graduate students at CMU.